34 citations · 63 across the 26 of their papers we have counts for
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cs.CR2023
Certified private data release for sparse Lipschitz functions
Konstantin Donhauser, Johan Lokna, Amartya Sanyal +3
As machine learning has become more relevant for everyday applications, a natural requirement is the protection of the privacy of the training data. When the relevant learning ques…
math.PR2023
Covariance loss, Szemeredi regularity, and differential privacy
March Boedihardjo, Thomas Strohmer, Roman Vershynin
We show how randomized rounding based on Grothendieck's identity can be used to prove a nearly tight bound on the covariance loss--the amount of covariance that is lost by taking c…